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What techniques are used to reduce false positives in machine learning models?

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Techniques used to reduce false positives in machine learning models include data pre-processing, feature selection, feature engineering, and proper evaluation metrics. Data pre-processing can be used to remove outliers that may lead to misclassification. Feature selection can be used to determine which features are most significant for prediction and to remove features that have a low correlation with the target. Feature engineering involves transforming existing features to better capture patterns in the data. Finally, proper evaluation metrics, such as precision and recall, should be used to measure the model’s performance.